The Future of ChatGPT: Exploring Features, Capabilities, and Challenges as a Leading Support Tool

Introduction

The swift advancement in natural language processing (NLP) and artificial intelligence (AI) has led to the creation of increasingly complex and adaptable language models, such as ChatGPT (McGee, 2023; Hill-Yardin et al., 2023; Barua et al., 2023). A branch of AI known as “generative AI” deals with models that can produce new data from preexisting data in a variety of domains, including text, images, and music, by using learnt patterns and structures (Johnson et al., 2023; Biswas et al., 2023; Merlin Mancy et al., 2024). These generative AI models, like ChatGPT, use deep learning techniques and neural networks to evaluate, understand, and produce material that appears to be human-generated. It delves into the specific features and capabilities of the ChatGPT Support System (Hill-Yardin et al., 2023; Datta et al., 2019). Finally, explore and discuss the pivotal roles played by ChatGPT in the contemporary landscape. The neural language models underpinning character AI have been meticulously designed to understand and generate human-like text. This technology processes and creates text using DL methods, utilizing vast internet databases to understand the subtleties of natural language (Johnson et al., 2023).

Figure 1.

Creating content using ChatGPT

(Oatug, 2023)

To understand ChatGPT's significance in advancing scientific research (Wu et al., 2023; Zhou et al., 2023), it is essential to delve into its origins and development (Bang et al., 2023; Van Dis et al., 2023). The following part gives a summary of ChatGPT's history, significant achievements, and ongoing developments, highlighting how technical developments have contributed to the platform's success in the scientific field (Qin et al., 2023; Gilson et al., 2023). Unlike Generative Adversarial Network (GAN) models, ChatGPT is a language model based on the Generative Pre-trained Transformer (GPT) architecture (Kung et al., 2023; Zhong et al.,, 2023; Jiao and others, 2023). GPT models, such as ChatGPT, are intended for natural language processing tasks including text generation and language understanding, even if GANs are frequently employed for image generation (Tlili et al., 2023; Alberts et al., 2023; Sharma et al., 2023).

Rooted in the field of NLP, the goal of ChatGPT is to allow machines to produce and comprehend human language (Fijačko et al., 2023; Barua et al., 2023). The goal of developing a very complex and adaptable AI language model that could help with data analysis, translation, and text creation drove its development (Sharma et al., 2016). The foundational architecture of ChatGPT, the Transformer, addressed limitations of previous models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for natural language processing, paving the way for powerful language models such as OpenAI's GPT series, which includes GPT-2 and GPT-3.

OpenAI introduced ChatGPT in 2020 as a revised form of the GPT-3 model, based on the GPT-3.5 architecture. GPT-3.5 maintains high performance in a variety of natural language processing (NLP) tasks, such as text generation, machine translation, and language understanding, with 6.7 billion parameters as opposed to 175 billion for GPT-3 (Alkaissi, et al., 2023). In 2023, Frieder et al. Figure 1 illustrates how to use ChatGPT to generate content. ChatGPT is an excellent tool for producing conversational responses to user inquiries because it has been trained on a big corpus of text data and refined for doing so (Cotton et al., 2023; Anu Baidoo et al., 2023].